Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations6400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory200.0 B

Variable types

Categorical8
Numeric17

Alerts

AirTemp is highly overall correlated with AvgStraightEntrySpeed and 7 other fieldsHigh correlation
AvgCornerEntrySpeed is highly overall correlated with AvgCornerSpeed and 1 other fieldsHigh correlation
AvgCornerSpeed is highly overall correlated with AvgCornerEntrySpeed and 10 other fieldsHigh correlation
AvgStraightEntrySpeed is highly overall correlated with AirTemp and 5 other fieldsHigh correlation
AvgStraightExitSpeed is highly overall correlated with AirTemp and 5 other fieldsHigh correlation
AvgStraightSpeed is highly overall correlated with AvgCornerEntrySpeed and 6 other fieldsHigh correlation
BrakeMean is highly overall correlated with AvgCornerSpeed and 4 other fieldsHigh correlation
BrakeStdDev is highly overall correlated with AvgCornerSpeed and 4 other fieldsHigh correlation
Driver is highly overall correlated with TeamHigh correlation
Event is highly overall correlated with AirTemp and 11 other fieldsHigh correlation
LapTimeSeconds is highly overall correlated with Event and 4 other fieldsHigh correlation
NumLaps is highly overall correlated with AirTemp and 13 other fieldsHigh correlation
RaceDistance is highly overall correlated with AirTemp and 11 other fieldsHigh correlation
Team is highly overall correlated with DriverHigh correlation
ThrottleMean is highly overall correlated with AvgCornerSpeed and 7 other fieldsHigh correlation
ThrottleStdDev is highly overall correlated with AvgCornerSpeed and 3 other fieldsHigh correlation
TopSpeed is highly overall correlated with AvgCornerSpeed and 5 other fieldsHigh correlation
TrackLength is highly overall correlated with AirTemp and 11 other fieldsHigh correlation
TrackTemp is highly overall correlated with AirTemp and 7 other fieldsHigh correlation
TyreCompound is highly overall correlated with TrackTempHigh correlation
Year is highly overall correlated with AirTemp and 1 other fieldsHigh correlation
ThrottleStdDev has unique values Unique
DRSMean has 5336 (83.4%) zeros Zeros

Reproduction

Analysis started2025-10-17 09:24:08.905704
Analysis finished2025-10-17 09:24:24.110028
Duration15.2 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Driver
Categorical

High correlation 

Distinct24
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
VER
 
442
PIA
 
374
LEC
 
370
RUS
 
365
NOR
 
364
Other values (19)
4485 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters19200
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVER
2nd rowVER
3rd rowVER
4th rowVER
5th rowVER

Common Values

ValueCountFrequency (%)
VER 442
 
6.9%
PIA 374
 
5.8%
LEC 370
 
5.8%
RUS 365
 
5.7%
NOR 364
 
5.7%
SAI 349
 
5.5%
HUL 344
 
5.4%
TSU 342
 
5.3%
ZHO 324
 
5.1%
HAM 308
 
4.8%
Other values (14) 2818
44.0%

Length

2025-10-17T16:24:24.144807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ver 442
 
6.9%
pia 374
 
5.8%
lec 370
 
5.8%
rus 365
 
5.7%
nor 364
 
5.7%
sai 349
 
5.5%
hul 344
 
5.4%
tsu 342
 
5.3%
zho 324
 
5.1%
ham 308
 
4.8%
Other values (14) 2818
44.0%

Most occurring characters

ValueCountFrequency (%)
A 2517
13.1%
R 1970
10.3%
O 1946
10.1%
S 1774
 
9.2%
L 1554
 
8.1%
E 1114
 
5.8%
U 1051
 
5.5%
H 976
 
5.1%
T 915
 
4.8%
I 847
 
4.4%
Other values (10) 4536
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2517
13.1%
R 1970
10.3%
O 1946
10.1%
S 1774
 
9.2%
L 1554
 
8.1%
E 1114
 
5.8%
U 1051
 
5.5%
H 976
 
5.1%
T 915
 
4.8%
I 847
 
4.4%
Other values (10) 4536
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2517
13.1%
R 1970
10.3%
O 1946
10.1%
S 1774
 
9.2%
L 1554
 
8.1%
E 1114
 
5.8%
U 1051
 
5.5%
H 976
 
5.1%
T 915
 
4.8%
I 847
 
4.4%
Other values (10) 4536
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2517
13.1%
R 1970
10.3%
O 1946
10.1%
S 1774
 
9.2%
L 1554
 
8.1%
E 1114
 
5.8%
U 1051
 
5.5%
H 976
 
5.1%
T 915
 
4.8%
I 847
 
4.4%
Other values (10) 4536
23.6%

Team
Categorical

High correlation 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
McLaren
738 
Ferrari
719 
Red Bull Racing
695 
Mercedes
673 
Haas F1 Team
649 
Other values (7)
2926 

Length

Max length15
Median length11
Mean length9.1415625
Min length2

Characters and Unicode

Total characters58506
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRed Bull Racing
2nd rowRed Bull Racing
3rd rowRed Bull Racing
4th rowRed Bull Racing
5th rowRed Bull Racing

Common Values

ValueCountFrequency (%)
McLaren 738
11.5%
Ferrari 719
11.2%
Red Bull Racing 695
10.9%
Mercedes 673
10.5%
Haas F1 Team 649
10.1%
Alpine 576
9.0%
Aston Martin 573
9.0%
Williams 456
7.1%
Kick Sauber 360
5.6%
RB 355
5.5%
Other values (2) 606
9.5%

Length

2025-10-17T16:24:24.191595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mclaren 738
 
7.2%
ferrari 719
 
7.0%
red 695
 
6.8%
bull 695
 
6.8%
racing 695
 
6.8%
mercedes 673
 
6.5%
haas 649
 
6.3%
f1 649
 
6.3%
team 649
 
6.3%
alpine 576
 
5.6%
Other values (9) 3545
34.5%

Most occurring characters

ValueCountFrequency (%)
a 6438
 
11.0%
e 6018
 
10.3%
r 4845
 
8.3%
i 4179
 
7.1%
3883
 
6.6%
l 3484
 
6.0%
n 3155
 
5.4%
c 2466
 
4.2%
s 2351
 
4.0%
R 2007
 
3.4%
Other values (22) 19680
33.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6438
 
11.0%
e 6018
 
10.3%
r 4845
 
8.3%
i 4179
 
7.1%
3883
 
6.6%
l 3484
 
6.0%
n 3155
 
5.4%
c 2466
 
4.2%
s 2351
 
4.0%
R 2007
 
3.4%
Other values (22) 19680
33.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6438
 
11.0%
e 6018
 
10.3%
r 4845
 
8.3%
i 4179
 
7.1%
3883
 
6.6%
l 3484
 
6.0%
n 3155
 
5.4%
c 2466
 
4.2%
s 2351
 
4.0%
R 2007
 
3.4%
Other values (22) 19680
33.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6438
 
11.0%
e 6018
 
10.3%
r 4845
 
8.3%
i 4179
 
7.1%
3883
 
6.6%
l 3484
 
6.0%
n 3155
 
5.4%
c 2466
 
4.2%
s 2351
 
4.0%
R 2007
 
3.4%
Other values (22) 19680
33.6%

Event
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
Brazilian Grand Prix
1476 
Abu Dhabi Grand Prix
1470 
Mexico City Grand Prix
1443 
Qatar Grand Prix
1106 
Las Vegas Grand Prix
905 

Length

Max length22
Median length20
Mean length19.759687
Min length16

Characters and Unicode

Total characters126462
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico City Grand Prix
2nd rowMexico City Grand Prix
3rd rowMexico City Grand Prix
4th rowMexico City Grand Prix
5th rowMexico City Grand Prix

Common Values

ValueCountFrequency (%)
Brazilian Grand Prix 1476
23.1%
Abu Dhabi Grand Prix 1470
23.0%
Mexico City Grand Prix 1443
22.5%
Qatar Grand Prix 1106
17.3%
Las Vegas Grand Prix 905
14.1%

Length

2025-10-17T16:24:24.237241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:24.280065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
grand 6400
27.8%
prix 6400
27.8%
brazilian 1476
 
6.4%
abu 1470
 
6.4%
dhabi 1470
 
6.4%
mexico 1443
 
6.3%
city 1443
 
6.3%
qatar 1106
 
4.8%
las 905
 
3.9%
vegas 905
 
3.9%

Most occurring characters

ValueCountFrequency (%)
16618
13.1%
r 15382
12.2%
a 14844
11.7%
i 13708
10.8%
n 7876
 
6.2%
x 7843
 
6.2%
G 6400
 
5.1%
d 6400
 
5.1%
P 6400
 
5.1%
b 2940
 
2.3%
Other values (19) 28051
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16618
13.1%
r 15382
12.2%
a 14844
11.7%
i 13708
10.8%
n 7876
 
6.2%
x 7843
 
6.2%
G 6400
 
5.1%
d 6400
 
5.1%
P 6400
 
5.1%
b 2940
 
2.3%
Other values (19) 28051
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16618
13.1%
r 15382
12.2%
a 14844
11.7%
i 13708
10.8%
n 7876
 
6.2%
x 7843
 
6.2%
G 6400
 
5.1%
d 6400
 
5.1%
P 6400
 
5.1%
b 2940
 
2.3%
Other values (19) 28051
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16618
13.1%
r 15382
12.2%
a 14844
11.7%
i 13708
10.8%
n 7876
 
6.2%
x 7843
 
6.2%
G 6400
 
5.1%
d 6400
 
5.1%
P 6400
 
5.1%
b 2940
 
2.3%
Other values (19) 28051
22.2%

Year
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
2024
3311 
2023
3089 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters25600
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2024 3311
51.7%
2023 3089
48.3%

Length

2025-10-17T16:24:24.326595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:24.360434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2024 3311
51.7%
2023 3089
48.3%

Most occurring characters

ValueCountFrequency (%)
2 12800
50.0%
0 6400
25.0%
4 3311
 
12.9%
3 3089
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 12800
50.0%
0 6400
25.0%
4 3311
 
12.9%
3 3089
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 12800
50.0%
0 6400
25.0%
4 3311
 
12.9%
3 3089
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 12800
50.0%
0 6400
25.0%
4 3311
 
12.9%
3 3089
 
12.1%

LapTimeSeconds
Real number (ℝ)

High correlation 

Distinct5352
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.048072
Minimum72.486
Maximum148.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:24.400716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum72.486
5-th percentile76.00485
Q182.9825
median86.3735
Q390.057
95-th percentile99.0751
Maximum148.49
Range76.004
Interquartile range (IQR)7.0745

Descriptive statistics

Standard deviation6.834553
Coefficient of variation (CV)0.078514698
Kurtosis4.7130521
Mean87.048072
Median Absolute Deviation (MAD)3.5345
Skewness1.024773
Sum557107.66
Variance46.711115
MonotonicityNot monotonic
2025-10-17T16:24:24.452582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.756 5
 
0.1%
83.859 4
 
0.1%
83.118 4
 
0.1%
85.279 4
 
0.1%
89.784 4
 
0.1%
82.815 4
 
0.1%
90.631 4
 
0.1%
83.473 4
 
0.1%
84.896 4
 
0.1%
84.657 4
 
0.1%
Other values (5342) 6359
99.4%
ValueCountFrequency (%)
72.486 1
< 0.1%
73.323 1
< 0.1%
73.367 1
< 0.1%
73.373 1
< 0.1%
73.422 1
< 0.1%
73.568 1
< 0.1%
73.586 1
< 0.1%
73.668 1
< 0.1%
73.676 1
< 0.1%
73.687 1
< 0.1%
ValueCountFrequency (%)
148.49 1
< 0.1%
141.611 1
< 0.1%
141.168 1
< 0.1%
138.655 1
< 0.1%
137.757 1
< 0.1%
133.022 1
< 0.1%
132.67 1
< 0.1%
130.857 1
< 0.1%
129.393 1
< 0.1%
129.188 1
< 0.1%

TyreCompound
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
HARD
3034 
MEDIUM
2081 
INTERMEDIATE
791 
SOFT
493 
WET
 
1

Length

Max length12
Median length4
Mean length5.6389062
Min length3

Characters and Unicode

Total characters36089
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMEDIUM
2nd rowMEDIUM
3rd rowMEDIUM
4th rowMEDIUM
5th rowMEDIUM

Common Values

ValueCountFrequency (%)
HARD 3034
47.4%
MEDIUM 2081
32.5%
INTERMEDIATE 791
 
12.4%
SOFT 493
 
7.7%
WET 1
 
< 0.1%

Length

2025-10-17T16:24:24.503671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:24.540437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
hard 3034
47.4%
medium 2081
32.5%
intermediate 791
 
12.4%
soft 493
 
7.7%
wet 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 5906
16.4%
M 4953
13.7%
E 4455
12.3%
A 3825
10.6%
R 3825
10.6%
I 3663
10.1%
H 3034
8.4%
U 2081
 
5.8%
T 2076
 
5.8%
N 791
 
2.2%
Other values (4) 1480
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 5906
16.4%
M 4953
13.7%
E 4455
12.3%
A 3825
10.6%
R 3825
10.6%
I 3663
10.1%
H 3034
8.4%
U 2081
 
5.8%
T 2076
 
5.8%
N 791
 
2.2%
Other values (4) 1480
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 5906
16.4%
M 4953
13.7%
E 4455
12.3%
A 3825
10.6%
R 3825
10.6%
I 3663
10.1%
H 3034
8.4%
U 2081
 
5.8%
T 2076
 
5.8%
N 791
 
2.2%
Other values (4) 1480
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 5906
16.4%
M 4953
13.7%
E 4455
12.3%
A 3825
10.6%
R 3825
10.6%
I 3663
10.1%
H 3034
8.4%
U 2081
 
5.8%
T 2076
 
5.8%
N 791
 
2.2%
Other values (4) 1480
 
4.1%

TyreAge
Real number (ℝ)

Distinct49
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.125312
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:24.586587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q19
median15
Q322
95-th percentile34
Maximum50
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3125774
Coefficient of variation (CV)0.57751299
Kurtosis-0.039054875
Mean16.125312
Median Absolute Deviation (MAD)7
Skewness0.64302008
Sum103202
Variance86.724097
MonotonicityNot monotonic
2025-10-17T16:24:24.636940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
13 280
 
4.4%
12 263
 
4.1%
16 261
 
4.1%
14 260
 
4.1%
7 258
 
4.0%
15 257
 
4.0%
17 257
 
4.0%
8 256
 
4.0%
10 248
 
3.9%
9 245
 
3.8%
Other values (39) 3815
59.6%
ValueCountFrequency (%)
2 196
3.1%
3 202
3.2%
4 183
2.9%
5 216
3.4%
6 225
3.5%
7 258
4.0%
8 256
4.0%
9 245
3.8%
10 248
3.9%
11 240
3.8%
ValueCountFrequency (%)
50 1
 
< 0.1%
49 2
 
< 0.1%
48 2
 
< 0.1%
47 3
 
< 0.1%
46 4
 
0.1%
45 6
 
0.1%
44 6
 
0.1%
43 12
0.2%
42 14
0.2%
41 20
0.3%

AirTemp
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.017062
Minimum17
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:24.682716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17.6
Q119.875
median21.1
Q326.5
95-th percentile31.2
Maximum31.5
Range14.5
Interquartile range (IQR)6.625

Descriptive statistics

Standard deviation4.2248803
Coefficient of variation (CV)0.18355428
Kurtosis-0.9905656
Mean23.017062
Median Absolute Deviation (MAD)3.4
Skewness0.42940234
Sum147309.2
Variance17.849613
MonotonicityNot monotonic
2025-10-17T16:24:24.730895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.5 384
 
6.0%
26.6 371
 
5.8%
26.5 349
 
5.5%
26.7 241
 
3.8%
20.4 211
 
3.3%
26.4 179
 
2.8%
18 163
 
2.5%
26.8 163
 
2.5%
20 159
 
2.5%
18.1 155
 
2.4%
Other values (67) 4025
62.9%
ValueCountFrequency (%)
17 20
 
0.3%
17.1 83
1.3%
17.2 26
 
0.4%
17.3 75
1.2%
17.4 36
 
0.6%
17.5 57
0.9%
17.6 120
1.9%
17.7 42
 
0.7%
17.8 9
 
0.1%
17.9 75
1.2%
ValueCountFrequency (%)
31.5 78
1.2%
31.4 89
1.4%
31.3 117
1.8%
31.2 115
1.8%
31.1 69
1.1%
31 95
1.5%
30.9 48
0.8%
27.1 8
 
0.1%
27 28
 
0.4%
26.9 94
1.5%

TrackTemp
Real number (ℝ)

High correlation 

Distinct193
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.584187
Minimum16.7
Maximum50.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:24.780533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile17.2
Q124.5
median31.3
Q337.3
95-th percentile47.5
Maximum50.2
Range33.5
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation9.2411441
Coefficient of variation (CV)0.29258768
Kurtosis-0.8224584
Mean31.584187
Median Absolute Deviation (MAD)6.5
Skewness0.14347878
Sum202138.8
Variance85.398744
MonotonicityNot monotonic
2025-10-17T16:24:24.834637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 186
 
2.9%
24.9 158
 
2.5%
17.7 134
 
2.1%
31.3 127
 
2.0%
22 126
 
2.0%
31.1 117
 
1.8%
31.2 106
 
1.7%
24.6 101
 
1.6%
17.8 99
 
1.5%
24.5 98
 
1.5%
Other values (183) 5148
80.4%
ValueCountFrequency (%)
16.7 89
1.4%
16.8 66
1.0%
16.9 38
0.6%
17 71
1.1%
17.1 46
0.7%
17.2 40
0.6%
17.3 40
0.6%
17.4 49
0.8%
17.5 48
0.8%
17.6 58
0.9%
ValueCountFrequency (%)
50.2 6
 
0.1%
49.8 16
0.2%
49.7 9
 
0.1%
49.6 4
 
0.1%
49.4 10
0.2%
49.3 3
 
< 0.1%
49.2 5
 
0.1%
49.1 14
0.2%
49 8
 
0.1%
48.9 24
0.4%

TrackLength
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
4.309
1476 
5.554
1470 
4.304
1443 
5.419
1106 
6.12
905 

Length

Max length5
Median length5
Mean length4.8585937
Min length4

Characters and Unicode

Total characters31095
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.304
2nd row4.304
3rd row4.304
4th row4.304
5th row4.304

Common Values

ValueCountFrequency (%)
4.309 1476
23.1%
5.554 1470
23.0%
4.304 1443
22.5%
5.419 1106
17.3%
6.12 905
14.1%

Length

2025-10-17T16:24:24.888423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:24.930643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.309 1476
23.1%
5.554 1470
23.0%
4.304 1443
22.5%
5.419 1106
17.3%
6.12 905
14.1%

Most occurring characters

ValueCountFrequency (%)
4 6938
22.3%
. 6400
20.6%
5 5516
17.7%
3 2919
9.4%
0 2919
9.4%
9 2582
 
8.3%
1 2011
 
6.5%
6 905
 
2.9%
2 905
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31095
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 6938
22.3%
. 6400
20.6%
5 5516
17.7%
3 2919
9.4%
0 2919
9.4%
9 2582
 
8.3%
1 2011
 
6.5%
6 905
 
2.9%
2 905
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31095
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 6938
22.3%
. 6400
20.6%
5 5516
17.7%
3 2919
9.4%
0 2919
9.4%
9 2582
 
8.3%
1 2011
 
6.5%
6 905
 
2.9%
2 905
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31095
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 6938
22.3%
. 6400
20.6%
5 5516
17.7%
3 2919
9.4%
0 2919
9.4%
9 2582
 
8.3%
1 2011
 
6.5%
6 905
 
2.9%
2 905
 
2.9%

RaceDistance
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
305.879
1476 
305.355
1470 
305.354
1443 
308.883
1106 
305.88
905 

Length

Max length7
Median length7
Mean length6.8585937
Min length6

Characters and Unicode

Total characters43895
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row305.354
2nd row305.354
3rd row305.354
4th row305.354
5th row305.354

Common Values

ValueCountFrequency (%)
305.879 1476
23.1%
305.355 1470
23.0%
305.354 1443
22.5%
308.883 1106
17.3%
305.88 905
14.1%

Length

2025-10-17T16:24:24.976732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:25.017397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
305.879 1476
23.1%
305.355 1470
23.0%
305.354 1443
22.5%
308.883 1106
17.3%
305.88 905
14.1%

Most occurring characters

ValueCountFrequency (%)
3 10419
23.7%
5 9677
22.0%
8 6604
15.0%
0 6400
14.6%
. 6400
14.6%
7 1476
 
3.4%
9 1476
 
3.4%
4 1443
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43895
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 10419
23.7%
5 9677
22.0%
8 6604
15.0%
0 6400
14.6%
. 6400
14.6%
7 1476
 
3.4%
9 1476
 
3.4%
4 1443
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43895
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 10419
23.7%
5 9677
22.0%
8 6604
15.0%
0 6400
14.6%
. 6400
14.6%
7 1476
 
3.4%
9 1476
 
3.4%
4 1443
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43895
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 10419
23.7%
5 9677
22.0%
8 6604
15.0%
0 6400
14.6%
. 6400
14.6%
7 1476
 
3.4%
9 1476
 
3.4%
4 1443
 
3.3%

NumLaps
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.1 KiB
71
2919 
55
1470 
57
1106 
50
905 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters12800
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row71
2nd row71
3rd row71
4th row71
5th row71

Common Values

ValueCountFrequency (%)
71 2919
45.6%
55 1470
23.0%
57 1106
 
17.3%
50 905
 
14.1%

Length

2025-10-17T16:24:25.061886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T16:24:25.098010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
71 2919
45.6%
55 1470
23.0%
57 1106
 
17.3%
50 905
 
14.1%

Most occurring characters

ValueCountFrequency (%)
5 4951
38.7%
7 4025
31.4%
1 2919
22.8%
0 905
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 4951
38.7%
7 4025
31.4%
1 2919
22.8%
0 905
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 4951
38.7%
7 4025
31.4%
1 2919
22.8%
0 905
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 4951
38.7%
7 4025
31.4%
1 2919
22.8%
0 905
 
7.1%

LapNumber
Real number (ℝ)

Distinct70
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.460781
Minimum2
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.171956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q120
median34
Q349
95-th percentile64
Maximum71
Range69
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.749565
Coefficient of variation (CV)0.51506566
Kurtosis-1.0318459
Mean34.460781
Median Absolute Deviation (MAD)15
Skewness0.097508686
Sum220549
Variance315.04706
MonotonicityNot monotonic
2025-10-17T16:24:25.230972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 136
 
2.1%
50 134
 
2.1%
23 132
 
2.1%
19 132
 
2.1%
21 130
 
2.0%
49 128
 
2.0%
45 128
 
2.0%
20 126
 
2.0%
22 126
 
2.0%
18 125
 
2.0%
Other values (60) 5103
79.7%
ValueCountFrequency (%)
2 59
0.9%
3 30
 
0.5%
4 36
 
0.6%
5 60
0.9%
6 73
1.1%
7 108
1.7%
8 87
1.4%
9 88
1.4%
10 88
1.4%
11 76
1.2%
ValueCountFrequency (%)
71 23
0.4%
70 31
0.5%
69 53
0.8%
68 56
0.9%
67 42
0.7%
66 46
0.7%
65 50
0.8%
64 53
0.8%
63 51
0.8%
62 52
0.8%

TopSpeed
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.19281
Minimum252
Maximum348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.283085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile301
Q1306
median311
Q3328
95-th percentile338
Maximum348
Range96
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.630912
Coefficient of variation (CV)0.039946866
Kurtosis-0.79092156
Mean316.19281
Median Absolute Deviation (MAD)7
Skewness0.41697736
Sum2023634
Variance159.53994
MonotonicityNot monotonic
2025-10-17T16:24:25.330942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
305 359
 
5.6%
307 347
 
5.4%
310 345
 
5.4%
306 319
 
5.0%
308 314
 
4.9%
309 305
 
4.8%
311 270
 
4.2%
304 242
 
3.8%
303 225
 
3.5%
312 215
 
3.4%
Other values (60) 3459
54.0%
ValueCountFrequency (%)
252 1
< 0.1%
259 1
< 0.1%
265 1
< 0.1%
268 2
< 0.1%
271 1
< 0.1%
272 1
< 0.1%
273 1
< 0.1%
278 1
< 0.1%
281 1
< 0.1%
283 1
< 0.1%
ValueCountFrequency (%)
348 1
 
< 0.1%
346 2
 
< 0.1%
345 7
 
0.1%
344 13
 
0.2%
343 19
 
0.3%
342 37
 
0.6%
341 43
0.7%
340 67
1.0%
339 100
1.6%
338 100
1.6%

AvgCornerSpeed
Real number (ℝ)

High correlation 

Distinct6161
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.45696
Minimum63.410959
Maximum166.9625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.379581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum63.410959
5-th percentile122.80976
Q1127.96901
median133.67902
Q3139.31436
95-th percentile161.6355
Maximum166.9625
Range103.55154
Interquartile range (IQR)11.345351

Descriptive statistics

Standard deviation12.625712
Coefficient of variation (CV)0.092525233
Kurtosis1.523527
Mean136.45696
Median Absolute Deviation (MAD)5.657754
Skewness0.57663156
Sum873324.54
Variance159.4086
MonotonicityNot monotonic
2025-10-17T16:24:25.432592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 8
 
0.1%
140 4
 
0.1%
137 4
 
0.1%
138.6666667 4
 
0.1%
162 4
 
0.1%
129 3
 
< 0.1%
136 3
 
< 0.1%
135.4160584 3
 
< 0.1%
123.7 3
 
< 0.1%
125 3
 
< 0.1%
Other values (6151) 6361
99.4%
ValueCountFrequency (%)
63.4109589 1
< 0.1%
64.01114206 1
< 0.1%
64.93147752 1
< 0.1%
67.27863777 1
< 0.1%
67.2983683 1
< 0.1%
67.85779817 1
< 0.1%
68.69794721 1
< 0.1%
71.9816092 1
< 0.1%
72.02694611 1
< 0.1%
75.35321101 1
< 0.1%
ValueCountFrequency (%)
166.9625 1
< 0.1%
166.1818182 1
< 0.1%
165.9494949 1
< 0.1%
165.9191919 1
< 0.1%
165.8072289 1
< 0.1%
165.5566038 1
< 0.1%
165.3854167 1
< 0.1%
165.2560976 1
< 0.1%
165.0873786 1
< 0.1%
165.0186916 1
< 0.1%

AvgCornerEntrySpeed
Real number (ℝ)

High correlation 

Distinct319
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.08666
Minimum4.6
Maximum196.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.482921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile141.6
Q1153.2
median161.6
Q3168.6
95-th percentile175.6
Maximum196.4
Range191.8
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation12.364514
Coefficient of variation (CV)0.077236379
Kurtosis22.295177
Mean160.08666
Median Absolute Deviation (MAD)7.6
Skewness-2.7312457
Sum1024554.6
Variance152.8812
MonotonicityNot monotonic
2025-10-17T16:24:25.534874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.2 63
 
1.0%
163.6 62
 
1.0%
166 62
 
1.0%
168.6 56
 
0.9%
166.6 56
 
0.9%
162 55
 
0.9%
161.2 54
 
0.8%
164.4 53
 
0.8%
164.8 52
 
0.8%
161.8 51
 
0.8%
Other values (309) 5836
91.2%
ValueCountFrequency (%)
4.6 1
< 0.1%
20.8 1
< 0.1%
30.4 1
< 0.1%
36 1
< 0.1%
37.4 1
< 0.1%
43 1
< 0.1%
43.2 1
< 0.1%
45.4 1
< 0.1%
49.6 1
< 0.1%
51 1
< 0.1%
ValueCountFrequency (%)
196.4 1
 
< 0.1%
189.8 1
 
< 0.1%
186 1
 
< 0.1%
185.8 1
 
< 0.1%
184.8 1
 
< 0.1%
183.8 2
< 0.1%
183.6 3
< 0.1%
183.2 2
< 0.1%
182.8 2
< 0.1%
182.4 2
< 0.1%

AvgCornerExitSpeed
Real number (ℝ)

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.57616
Minimum99.8
Maximum196.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.587410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum99.8
5-th percentile165
Q1172
median176.2
Q3180
95-th percentile184.4
Maximum196.6
Range96.8
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.3734035
Coefficient of variation (CV)0.036299937
Kurtosis7.4796415
Mean175.57616
Median Absolute Deviation (MAD)4
Skewness-1.3157693
Sum1123687.4
Variance40.620272
MonotonicityNot monotonic
2025-10-17T16:24:25.636524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.8 99
 
1.5%
176.8 98
 
1.5%
175.2 98
 
1.5%
176.4 96
 
1.5%
178 95
 
1.5%
178.6 95
 
1.5%
177 94
 
1.5%
176.2 93
 
1.5%
178.4 92
 
1.4%
176.6 91
 
1.4%
Other values (204) 5449
85.1%
ValueCountFrequency (%)
99.8 1
< 0.1%
110.4 1
< 0.1%
123 1
< 0.1%
133 1
< 0.1%
138 2
< 0.1%
138.6 1
< 0.1%
140 1
< 0.1%
140.6 1
< 0.1%
141 1
< 0.1%
143 1
< 0.1%
ValueCountFrequency (%)
196.6 2
< 0.1%
194.8 2
< 0.1%
192.8 1
 
< 0.1%
192.4 2
< 0.1%
192.2 1
 
< 0.1%
191 3
< 0.1%
190.4 1
 
< 0.1%
190 1
 
< 0.1%
189.8 2
< 0.1%
189.6 2
< 0.1%

AvgStraightSpeed
Real number (ℝ)

High correlation 

Distinct6202
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.56525
Minimum220.90741
Maximum291.35238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.684081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum220.90741
5-th percentile250.55015
Q1259.50658
median262.9375
Q3267.50863
95-th percentile285.92285
Maximum291.35238
Range70.444974
Interquartile range (IQR)8.0020481

Descriptive statistics

Standard deviation10.021186
Coefficient of variation (CV)0.037877939
Kurtosis0.55144514
Mean264.56525
Median Absolute Deviation (MAD)4.0506862
Skewness0.76225001
Sum1693217.6
Variance100.42418
MonotonicityNot monotonic
2025-10-17T16:24:25.736832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
261.5 5
 
0.1%
266 5
 
0.1%
269.3333333 4
 
0.1%
263 4
 
0.1%
261.0333333 3
 
< 0.1%
264.784 3
 
< 0.1%
266.2519084 3
 
< 0.1%
265.703125 3
 
< 0.1%
266.875 3
 
< 0.1%
266.4 3
 
< 0.1%
Other values (6192) 6364
99.4%
ValueCountFrequency (%)
220.9074074 1
< 0.1%
225.0116279 1
< 0.1%
227.3506494 1
< 0.1%
228.8970588 1
< 0.1%
230.0857143 1
< 0.1%
231.5416667 1
< 0.1%
231.625 1
< 0.1%
231.7901235 1
< 0.1%
232.2222222 1
< 0.1%
232.5617978 1
< 0.1%
ValueCountFrequency (%)
291.352381 1
< 0.1%
291.2433628 1
< 0.1%
291.0089686 1
< 0.1%
290.78125 1
< 0.1%
290.7079646 1
< 0.1%
290.4090909 1
< 0.1%
290.4080717 1
< 0.1%
290.3244444 1
< 0.1%
290.1428571 1
< 0.1%
290.0438596 1
< 0.1%

AvgStraightEntrySpeed
Real number (ℝ)

High correlation 

Distinct486
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.17319
Minimum208
Maximum335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.787909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum208
5-th percentile234.8
Q1260
median283.8
Q3306.2
95-th percentile321.2
Maximum335
Range127
Interquartile range (IQR)46.2

Descriptive statistics

Standard deviation29.348034
Coefficient of variation (CV)0.10512483
Kurtosis-1.2226094
Mean279.17319
Median Absolute Deviation (MAD)22.8
Skewness-0.16140456
Sum1786708.4
Variance861.30707
MonotonicityNot monotonic
2025-10-17T16:24:25.838802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
284 48
 
0.8%
283.6 46
 
0.7%
285.4 41
 
0.6%
235.8 40
 
0.6%
283.8 40
 
0.6%
285 39
 
0.6%
284.8 38
 
0.6%
266 38
 
0.6%
237.8 38
 
0.6%
284.4 37
 
0.6%
Other values (476) 5995
93.7%
ValueCountFrequency (%)
208 1
 
< 0.1%
208.2 1
 
< 0.1%
210 2
< 0.1%
211 2
< 0.1%
212.4 1
 
< 0.1%
213 2
< 0.1%
213.6 1
 
< 0.1%
214.6 1
 
< 0.1%
214.8 1
 
< 0.1%
215 3
< 0.1%
ValueCountFrequency (%)
335 1
 
< 0.1%
333.8 1
 
< 0.1%
332 1
 
< 0.1%
331.8 1
 
< 0.1%
331.4 3
< 0.1%
330.6 1
 
< 0.1%
330.2 1
 
< 0.1%
329.8 1
 
< 0.1%
329.2 2
< 0.1%
329 1
 
< 0.1%

AvgStraightExitSpeed
Real number (ℝ)

High correlation 

Distinct494
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.95206
Minimum202.4
Maximum332.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.887927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum202.4
5-th percentile213.2
Q1233.8
median272.2
Q3304.4
95-th percentile323.6
Maximum332.8
Range130.4
Interquartile range (IQR)70.6

Descriptive statistics

Standard deviation38.35099
Coefficient of variation (CV)0.14366246
Kurtosis-1.488923
Mean266.95206
Median Absolute Deviation (MAD)35.4
Skewness0.064103107
Sum1708493.2
Variance1470.7984
MonotonicityNot monotonic
2025-10-17T16:24:25.938150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
274 40
 
0.6%
236.4 38
 
0.6%
275 36
 
0.6%
274.4 36
 
0.6%
239.2 36
 
0.6%
272.6 36
 
0.6%
303.6 35
 
0.5%
272.8 35
 
0.5%
273.2 34
 
0.5%
273 34
 
0.5%
Other values (484) 6040
94.4%
ValueCountFrequency (%)
202.4 1
 
< 0.1%
204.2 1
 
< 0.1%
206.6 1
 
< 0.1%
207.2 3
< 0.1%
207.4 1
 
< 0.1%
207.6 2
 
< 0.1%
207.8 1
 
< 0.1%
208 4
0.1%
208.2 3
< 0.1%
208.4 7
0.1%
ValueCountFrequency (%)
332.8 2
 
< 0.1%
332.2 1
 
< 0.1%
332 1
 
< 0.1%
331.6 2
 
< 0.1%
331 1
 
< 0.1%
330.6 2
 
< 0.1%
330.2 2
 
< 0.1%
330 3
< 0.1%
329.8 5
0.1%
329.6 3
< 0.1%

ThrottleMean
Real number (ℝ)

High correlation 

Distinct6324
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.998856
Minimum30.820513
Maximum81.658147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:25.988594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30.820513
5-th percentile51.144496
Q155.864653
median64.93284
Q369.0007
95-th percentile73.902853
Maximum81.658147
Range50.837634
Interquartile range (IQR)13.136047

Descriptive statistics

Standard deviation7.7198377
Coefficient of variation (CV)0.12253933
Kurtosis-0.44719704
Mean62.998856
Median Absolute Deviation (MAD)5.7465325
Skewness-0.38978817
Sum403192.68
Variance59.595894
MonotonicityNot monotonic
2025-10-17T16:24:26.037373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.07443366 3
 
< 0.1%
56.36363636 2
 
< 0.1%
67.38596491 2
 
< 0.1%
66.99393939 2
 
< 0.1%
55.53015873 2
 
< 0.1%
56 2
 
< 0.1%
71.48484848 2
 
< 0.1%
68.75675676 2
 
< 0.1%
57.48589342 2
 
< 0.1%
54.29276316 2
 
< 0.1%
Other values (6314) 6379
99.7%
ValueCountFrequency (%)
30.82051282 1
< 0.1%
31.18900344 1
< 0.1%
31.98039216 1
< 0.1%
31.99630314 1
< 0.1%
32.70588235 1
< 0.1%
32.875 1
< 0.1%
33.29585799 1
< 0.1%
33.62962963 1
< 0.1%
34.19387755 1
< 0.1%
34.625 1
< 0.1%
ValueCountFrequency (%)
81.65814696 1
< 0.1%
81.63192182 1
< 0.1%
81.28930818 1
< 0.1%
80.82142857 1
< 0.1%
80.73397436 1
< 0.1%
80.61980831 1
< 0.1%
80.57863501 1
< 0.1%
80.41987179 1
< 0.1%
80.39936102 1
< 0.1%
80.36532508 1
< 0.1%

ThrottleStdDev
Real number (ℝ)

High correlation  Unique 

Distinct6400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.676212
Minimum27.706134
Maximum45.164108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:26.085040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum27.706134
5-th percentile38.853892
Q140.884099
median41.967191
Q342.816189
95-th percentile43.673379
Maximum45.164108
Range17.457974
Interquartile range (IQR)1.9320891

Descriptive statistics

Standard deviation1.6574038
Coefficient of variation (CV)0.03976858
Kurtosis6.8422616
Mean41.676212
Median Absolute Deviation (MAD)0.93340419
Skewness-1.7986175
Sum266727.76
Variance2.7469873
MonotonicityNot monotonic
2025-10-17T16:24:26.137544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.86120992 1
 
< 0.1%
42.11972147 1
 
< 0.1%
42.54629922 1
 
< 0.1%
43.10133393 1
 
< 0.1%
42.90142651 1
 
< 0.1%
41.9613871 1
 
< 0.1%
41.65318571 1
 
< 0.1%
42.12299926 1
 
< 0.1%
42.19867117 1
 
< 0.1%
41.38448468 1
 
< 0.1%
Other values (6390) 6390
99.8%
ValueCountFrequency (%)
27.70613352 1
< 0.1%
28.70633499 1
< 0.1%
29.05455419 1
< 0.1%
29.8475926 1
< 0.1%
30.31486673 1
< 0.1%
30.66593514 1
< 0.1%
30.87788332 1
< 0.1%
31.30362903 1
< 0.1%
31.73447635 1
< 0.1%
32.31150574 1
< 0.1%
ValueCountFrequency (%)
45.16410787 1
< 0.1%
44.95780944 1
< 0.1%
44.84615477 1
< 0.1%
44.84024251 1
< 0.1%
44.79405436 1
< 0.1%
44.77633751 1
< 0.1%
44.7380176 1
< 0.1%
44.6892816 1
< 0.1%
44.66708258 1
< 0.1%
44.64805522 1
< 0.1%

BrakeMean
Real number (ℝ)

High correlation 

Distinct2459
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19268772
Minimum0.099071207
Maximum0.50793651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:26.189492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.099071207
5-th percentile0.13855422
Q10.1625
median0.18661972
Q30.22295082
95-th percentile0.25316456
Maximum0.50793651
Range0.4088653
Interquartile range (IQR)0.06045082

Descriptive statistics

Standard deviation0.038561425
Coefficient of variation (CV)0.20012394
Kurtosis2.4736387
Mean0.19268772
Median Absolute Deviation (MAD)0.029383648
Skewness0.74745196
Sum1233.2014
Variance0.0014869835
MonotonicityNot monotonic
2025-10-17T16:24:26.243061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 36
 
0.6%
0.2222222222 30
 
0.5%
0.2 28
 
0.4%
0.1818181818 19
 
0.3%
0.25 18
 
0.3%
0.1606060606 15
 
0.2%
0.1538461538 15
 
0.2%
0.1578947368 15
 
0.2%
0.2307692308 14
 
0.2%
0.1428571429 14
 
0.2%
Other values (2449) 6196
96.8%
ValueCountFrequency (%)
0.09907120743 1
< 0.1%
0.1006493506 1
< 0.1%
0.1008902077 1
< 0.1%
0.1012658228 1
< 0.1%
0.1063829787 1
< 0.1%
0.1070336391 1
< 0.1%
0.1107692308 1
< 0.1%
0.1121495327 1
< 0.1%
0.1121794872 1
< 0.1%
0.1129032258 1
< 0.1%
ValueCountFrequency (%)
0.5079365079 1
< 0.1%
0.4828973843 1
< 0.1%
0.4754797441 1
< 0.1%
0.4694280079 1
< 0.1%
0.4613733906 1
< 0.1%
0.4200913242 1
< 0.1%
0.4174757282 1
< 0.1%
0.4089834515 1
< 0.1%
0.4061538462 1
< 0.1%
0.3898916968 1
< 0.1%

BrakeStdDev
Real number (ℝ)

High correlation 

Distinct2905
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39204788
Minimum0.29922115
Maximum0.50043372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:26.296605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.29922115
5-th percentile0.34600223
Q10.36947738
median0.3902937
Q30.41690962
95-th percentile0.43551406
Maximum0.50043372
Range0.20121257
Interquartile range (IQR)0.047432243

Descriptive statistics

Standard deviation0.029108809
Coefficient of variation (CV)0.074248095
Kurtosis-0.61481328
Mean0.39204788
Median Absolute Deviation (MAD)0.023910605
Skewness0.014721379
Sum2509.1065
Variance0.00084732275
MonotonicityNot monotonic
2025-10-17T16:24:26.349791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3677247831 15
 
0.2%
0.4166515291 13
 
0.2%
0.386046147 12
 
0.2%
0.3622169753 12
 
0.2%
0.3692175325 12
 
0.2%
0.4246237488 12
 
0.2%
0.4220018721 11
 
0.2%
0.419933193 11
 
0.2%
0.3641556598 11
 
0.2%
0.3705462841 11
 
0.2%
Other values (2895) 6280
98.1%
ValueCountFrequency (%)
0.2992211492 1
< 0.1%
0.3013534623 1
< 0.1%
0.3016311457 1
< 0.1%
0.3021588653 1
< 0.1%
0.308796818 1
< 0.1%
0.3096298138 1
< 0.1%
0.3143301112 1
< 0.1%
0.3160429996 1
< 0.1%
0.3160941186 1
< 0.1%
0.3169861466 1
< 0.1%
ValueCountFrequency (%)
0.5004337164 1
< 0.1%
0.5002108986 1
< 0.1%
0.4999316557 1
< 0.1%
0.4995573814 1
< 0.1%
0.4990414921 1
< 0.1%
0.4941377075 1
< 0.1%
0.4939426299 1
< 0.1%
0.4922283765 1
< 0.1%
0.4918712453 1
< 0.1%
0.48860826 1
< 0.1%

DRSMean
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16088368
Minimum0
Maximum1
Zeros5336
Zeros (%)83.4%
Negative0
Negative (%)0.0%
Memory size50.1 KiB
2025-10-17T16:24:26.404264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3667249
Coefficient of variation (CV)2.2794413
Kurtosis1.4249306
Mean0.16088368
Median Absolute Deviation (MAD)0
Skewness1.8496109
Sum1029.6555
Variance0.13448715
MonotonicityNot monotonic
2025-10-17T16:24:26.454307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5336
83.4%
1 991
 
15.5%
0.9971098266 7
 
0.1%
0.9971014493 5
 
0.1%
0.9974025974 3
 
< 0.1%
0.99669967 2
 
< 0.1%
0.07668711656 2
 
< 0.1%
0.9973958333 2
 
< 0.1%
0.3365079365 1
 
< 0.1%
0.02305475504 1
 
< 0.1%
Other values (50) 50
 
0.8%
ValueCountFrequency (%)
0 5336
83.4%
0.005102040816 1
 
< 0.1%
0.01136363636 1
 
< 0.1%
0.01714285714 1
 
< 0.1%
0.02121212121 1
 
< 0.1%
0.02208201893 1
 
< 0.1%
0.02305475504 1
 
< 0.1%
0.02590673575 1
 
< 0.1%
0.03058103976 1
 
< 0.1%
0.03353658537 1
 
< 0.1%
ValueCountFrequency (%)
1 991
15.5%
0.9974226804 1
 
< 0.1%
0.9974160207 1
 
< 0.1%
0.9974025974 3
 
< 0.1%
0.9973958333 2
 
< 0.1%
0.9973474801 1
 
< 0.1%
0.9973262032 1
 
< 0.1%
0.9972972973 1
 
< 0.1%
0.9971671388 1
 
< 0.1%
0.9971590909 1
 
< 0.1%

Interactions

2025-10-17T16:24:22.966340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.486154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.242784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.890032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.546862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.203934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.033411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.944249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.639181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.834457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.455825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.143912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.542952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.223107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.857810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.571646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.250629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.005167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.528192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.282594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.931603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.587683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.244828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.107648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.984287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.682203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.874463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.503682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.187290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.583956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.262412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.898722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.613812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.291267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.040278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.568200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.316467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.968405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.621975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.279811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.148118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.021267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.720324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.909567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.540285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.224520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.621607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.297631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.935436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.652516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.329288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.078836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.607596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.353584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.006215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.660313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.320906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.190679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.058451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.761205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.945775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.577869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.263754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.660671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.335635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.974961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.693660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.368696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.115004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.645976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.396731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.043129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.696991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.358117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.232839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.097849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.800562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.981162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.615043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.304526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.697482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.370854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.013815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.732878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.420145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.151569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.686603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.438405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.081513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.735720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.396422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.272563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.136961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.840441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.017695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.654138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.344138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.737494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.412636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.052236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.772819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.465993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.185355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.724039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.474090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.115620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.770238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.432221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.305283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.171915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.876053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.052207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.689003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.379645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.773078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.447053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.088861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.808886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.505916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.221104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.824080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.512935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.153130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.809970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.468617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.343446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.207053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.912373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.085336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.725055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.744863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.809260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.481018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.126196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.847468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.543967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.261959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.868328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.551660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.194094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.850456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.508574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.398815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.246651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.345587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.123856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.764423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.792516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.850552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.520990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.167260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.889823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.586232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.297012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.909601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.588540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.230291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.887558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.548835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.437760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.282290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.425213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.158904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.801359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.832159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.887072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.555876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.205000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.927500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.623752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.334864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.952801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.626474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.270517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.926710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.590723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.478285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.320546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.530276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.195310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.838508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.873771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.926165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.593020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.247071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.968448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.665793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.371590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:09.997332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.665858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.309137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.965340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.629510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:13.517540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.362616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.583351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.233058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.875717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.914431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.964179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.630251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.285118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.007587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.705404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.410229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.037260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.702215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.346817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.004271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.721884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.750139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.403429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.625571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.269821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.912692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.951645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.005948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.668305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.324479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.048386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.748483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.447950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.075279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.736772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.383261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.044071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.783683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.787395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.442093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.667272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.303937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.948994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.369745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.063451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.702583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.361262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.088760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.786047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.486440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.117273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.774573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.424329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.085389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.824575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.826532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.481262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.711441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.341195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.987266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.412698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.103151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.741605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.448383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.128892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.840996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.525667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.160137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.813426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.465290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.125079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.866687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.868145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.521442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.755072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.382001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.028701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.454264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.144707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.781110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.493241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.169690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.886473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:23.566964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.202229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:10.853155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:11.509660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.167607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:12.951493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:14.909515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:15.598836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:16.798115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:17.420758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:18.069521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:19.503324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.186400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:20.821110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:21.535847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.212393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-10-17T16:24:22.928543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-10-17T16:24:26.507549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AirTempAvgCornerEntrySpeedAvgCornerExitSpeedAvgCornerSpeedAvgStraightEntrySpeedAvgStraightExitSpeedAvgStraightSpeedBrakeMeanBrakeStdDevDRSMeanDriverEventLapNumberLapTimeSecondsNumLapsRaceDistanceTeamThrottleMeanThrottleStdDevTopSpeedTrackLengthTrackTempTyreAgeTyreCompoundYear
AirTemp1.0000.339-0.0770.284-0.642-0.643-0.493-0.175-0.175-0.0400.1520.781-0.0930.0520.8520.7810.133-0.037-0.148-0.3840.7810.529-0.1290.4500.524
AvgCornerEntrySpeed0.3391.0000.0870.664-0.228-0.239-0.520-0.483-0.4830.0220.0460.326-0.1130.3050.3570.3260.0300.439-0.495-0.4710.326-0.231-0.1210.0910.082
AvgCornerExitSpeed-0.0770.0871.0000.1360.1030.129-0.122-0.064-0.0640.0550.0280.113-0.1140.0220.1260.1130.0090.077-0.096-0.1000.113-0.077-0.0510.0710.046
AvgCornerSpeed0.2840.6640.1361.000-0.125-0.135-0.740-0.786-0.786-0.1880.1080.765-0.0890.2310.7800.7650.0650.728-0.572-0.6980.765-0.144-0.2190.2910.240
AvgStraightEntrySpeed-0.642-0.2280.103-0.1251.0000.9600.2920.1900.1900.3130.1470.9120.013-0.0230.9100.9120.101-0.0070.0820.2070.912-0.405-0.0310.4000.199
AvgStraightExitSpeed-0.643-0.2390.129-0.1350.9601.0000.2690.1950.1950.3390.1390.927-0.0150.0050.9240.9270.091-0.0170.0740.1860.927-0.397-0.0560.4350.227
AvgStraightSpeed-0.493-0.520-0.122-0.7400.2920.2691.0000.4850.4840.0060.1120.7580.1500.0880.8210.7580.091-0.3210.4020.8840.758-0.2610.1800.3040.257
BrakeMean-0.175-0.483-0.064-0.7860.1900.1950.4851.0001.0000.4510.1380.454-0.002-0.3460.5080.4540.099-0.8020.5330.4410.4540.1720.1600.2510.236
BrakeStdDev-0.175-0.483-0.064-0.7860.1900.1950.4841.0001.0000.4510.1630.485-0.001-0.3480.5360.4850.122-0.8030.5330.4400.4850.1730.1600.2640.243
DRSMean-0.0400.0220.055-0.1880.3130.3390.0060.4510.4511.0000.1120.296-0.110-0.0700.2090.2960.065-0.4620.031-0.0740.296-0.228-0.0010.4310.249
Driver0.1520.0460.0280.1080.1470.1390.1120.1380.1630.1121.0000.1850.0000.1010.1640.1850.9200.1740.1820.1570.1850.1480.0490.1860.321
Event0.7810.3260.1130.7650.9120.9270.7580.4540.4850.2960.1851.0000.2050.7151.0001.0000.1000.5440.3570.6021.0000.8400.1670.4620.082
LapNumber-0.093-0.113-0.114-0.0890.013-0.0150.150-0.002-0.001-0.1100.0000.2051.000-0.3750.2320.2050.000-0.0330.0450.0100.2050.0090.4600.2300.076
LapTimeSeconds0.0520.3050.0220.231-0.0230.0050.088-0.346-0.348-0.0700.1010.715-0.3751.0000.7670.7150.0800.479-0.3810.1890.715-0.580-0.2830.4120.416
NumLaps0.8520.3570.1260.7800.9100.9240.8210.5080.5360.2090.1641.0000.2320.7671.0001.0000.0840.6040.3940.5511.0000.8990.1670.3510.083
RaceDistance0.7810.3260.1130.7650.9120.9270.7580.4540.4850.2960.1851.0000.2050.7151.0001.0000.1000.5440.3570.6021.0000.8400.1670.4620.082
Team0.1330.0300.0090.0650.1010.0910.0910.0990.1220.0650.9200.1000.0000.0800.0840.1001.0000.1470.1530.1410.1000.1210.0520.1260.463
ThrottleMean-0.0370.4390.0770.728-0.007-0.017-0.321-0.802-0.803-0.4620.1740.544-0.0330.4790.6040.5440.1471.000-0.650-0.2730.544-0.386-0.1520.3510.276
ThrottleStdDev-0.148-0.495-0.096-0.5720.0820.0740.4020.5330.5330.0310.1820.3570.045-0.3810.3940.3570.153-0.6501.0000.3590.3570.3730.0940.1520.148
TopSpeed-0.384-0.471-0.100-0.6980.2070.1860.8840.4410.440-0.0740.1570.6020.0100.1890.5510.6020.141-0.2730.3591.0000.602-0.2090.0800.3540.263
TrackLength0.7810.3260.1130.7650.9120.9270.7580.4540.4850.2960.1851.0000.2050.7151.0001.0000.1000.5440.3570.6021.0000.8400.1670.4620.082
TrackTemp0.529-0.231-0.077-0.144-0.405-0.397-0.2610.1720.173-0.2280.1480.8400.009-0.5800.8990.8400.121-0.3860.373-0.2090.8401.000-0.0440.5700.663
TyreAge-0.129-0.121-0.051-0.219-0.031-0.0560.1800.1600.160-0.0010.0490.1670.460-0.2830.1670.1670.052-0.1520.0940.0800.167-0.0441.0000.1130.191
TyreCompound0.4500.0910.0710.2910.4000.4350.3040.2510.2640.4310.1860.4620.2300.4120.3510.4620.1260.3510.1520.3540.4620.5700.1131.0000.420
Year0.5240.0820.0460.2400.1990.2270.2570.2360.2430.2490.3210.0820.0760.4160.0830.0820.4630.2760.1480.2630.0820.6630.1910.4201.000

Missing values

2025-10-17T16:24:23.924032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-17T16:24:24.048071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DriverTeamEventYearLapTimeSecondsTyreCompoundTyreAgeAirTempTrackTempTrackLengthRaceDistanceNumLapsLapNumberTopSpeedAvgCornerSpeedAvgCornerEntrySpeedAvgCornerExitSpeedAvgStraightSpeedAvgStraightEntrySpeedAvgStraightExitSpeedThrottleMeanThrottleStdDevBrakeMeanBrakeStdDevDRSMean
0VERRed Bull RacingMexico City Grand Prix202383.859MEDIUM2.024.648.64.304305.354712.0318.0123.333333156.4179.6261.897059260.8235.852.89634143.8612100.2286590.4206101.000000
1VERRed Bull RacingMexico City Grand Prix202383.573MEDIUM3.024.848.24.304305.354713.0319.0126.151685160.6175.4266.203008258.8236.254.96784643.9227210.2090030.4072520.209003
2VERRed Bull RacingMexico City Grand Prix202383.708MEDIUM4.024.747.94.304305.354714.0321.0125.502646162.4172.8266.189394264.2236.253.44548343.8055820.2242990.4177710.000000
3VERRed Bull RacingMexico City Grand Prix202383.339MEDIUM6.025.047.04.304305.354716.0320.0124.842391162.0167.8264.903226266.0240.652.33441644.2847080.2337660.4239140.000000
4VERRed Bull RacingMexico City Grand Prix202383.453MEDIUM7.025.147.04.304305.354717.0322.0124.908108161.0182.6266.508475260.4227.653.53795443.7288190.2409240.4283520.000000
5VERRed Bull RacingMexico City Grand Prix202383.218MEDIUM9.025.447.24.304305.354719.0322.0124.123596145.4171.8265.059259260.0237.254.73801943.5115120.2172520.4130360.000000
6VERRed Bull RacingMexico City Grand Prix202383.276MEDIUM10.025.447.24.304305.3547110.0322.0125.677778141.8180.4265.000000263.6236.855.08832843.6331630.2271290.4196390.000000
7VERRed Bull RacingMexico City Grand Prix202383.626MEDIUM11.025.347.34.304305.3547111.0322.0124.157609146.0180.4263.992647259.2232.655.00000043.7254190.1875000.3909240.000000
8VERRed Bull RacingMexico City Grand Prix202383.644MEDIUM12.025.447.44.304305.3547112.0323.0123.183333163.6172.6263.606061261.0230.854.62500044.1326240.2243590.4178300.000000
9VERRed Bull RacingMexico City Grand Prix202383.792MEDIUM13.025.547.64.304305.3547113.0323.0123.450262157.8170.4264.261194259.6237.854.00923143.7821830.2246150.4179720.000000
DriverTeamEventYearLapTimeSecondsTyreCompoundTyreAgeAirTempTrackTempTrackLengthRaceDistanceNumLapsLapNumberTopSpeedAvgCornerSpeedAvgCornerEntrySpeedAvgCornerExitSpeedAvgStraightSpeedAvgStraightEntrySpeedAvgStraightExitSpeedThrottleMeanThrottleStdDevBrakeMeanBrakeStdDevDRSMean
6390COLWilliamsAbu Dhabi Grand Prix202490.464HARD13.026.631.25.554305.3555516.0316.0135.875862166.8168.6260.803109232.0218.661.11834341.8226890.1893490.3923670.0
6391COLWilliamsAbu Dhabi Grand Prix202490.197HARD14.026.731.35.554305.3555517.0315.0139.244755164.8170.2262.327957234.2225.262.63221941.5546400.1793310.3842140.0
6392COLWilliamsAbu Dhabi Grand Prix202490.259HARD15.026.631.45.554305.3555518.0315.0137.401408158.2175.6261.218085237.2222.263.19697041.3889620.1787880.3837560.0
6393COLWilliamsAbu Dhabi Grand Prix202490.441HARD16.026.731.35.554305.3555519.0313.0135.957746161.4182.8260.620321233.8227.863.85410340.8475380.1762920.3816490.0
6394COLWilliamsAbu Dhabi Grand Prix202490.066HARD17.026.631.35.554305.3555520.0315.0137.118881165.0169.6260.722222236.4211.663.21407641.1122340.1700880.3762620.0
6395COLWilliamsAbu Dhabi Grand Prix202490.149HARD18.026.631.05.554305.3555521.0314.0138.850340171.6179.4263.261780239.4217.663.99408341.3301820.1775150.3826700.0
6396COLWilliamsAbu Dhabi Grand Prix202490.317HARD19.026.631.15.554305.3555522.0313.0138.100671158.8169.0260.573684231.8224.062.03539841.1725180.1828910.3871480.0
6397COLWilliamsAbu Dhabi Grand Prix202490.387HARD20.026.531.15.554305.3555523.0315.0137.482517168.6171.4261.989950237.2219.864.14619941.3087470.1783630.3833790.0
6398COLWilliamsAbu Dhabi Grand Prix202490.528HARD21.026.530.95.554305.3555524.0313.0137.536913161.2174.6261.783784235.8229.661.47604841.6515400.1826350.3869460.0
6399COLWilliamsAbu Dhabi Grand Prix202490.254HARD22.026.530.95.554305.3555525.0313.0136.326531157.2177.2260.953846238.6213.062.32748541.5483790.1725150.3783810.0